Work

An analytical approach to assessing the spatial equity and allocation of healthcare resources in Shanghai

Year: 2025

Type: article

Abstract: The rational allocation of healthcare resources is vital for establishing a healthcare system that aligns with the levels of economic and social development. As a comprehensive discipline integrating geography, cartography, remote sensing, and computer science, Geographic Information System (GIS) can visualize and analyze spatial information through mapping. By utilizing GIS's statistical analysis and data visualization functions, this study provides a more efficient and intuitive analysis of Shanghai's spatial healthcare resource allocation and a more comprehensive assessment of its current allocation status. To examine the spatial correlation and spatial proximity, we apply the Global Moran Index (Moran's I), the Local Indicators of Spatial Association (LISA) test, and Hot Spot Analysis (Getis-Ord Gi∗) for assessment. Furthermore, by utilizing the Lorenz curve and Gini coefficient, this study provides a new perspective by expanding the measurement dimensions for assessing healthcare resource allocation in Shanghai. The results show that: From the global spatial correlation perspective, the allocation of healthcare resources in Shanghai exhibits spatial clustering. From the local spatial correlation perspective, healthcare resources in Shanghai show significant regional disparities, with resources concentrated in central urban areas. And from a multidimensional perspective, the equity of allocation of healthcare resources in Shanghai in 2022 was higher when measured by population (0.298 ± 0.063) and economy (0.292 ± 0.027) than by geographic area (0.612 ± 0.100) and green spaces (0.590 ± 0.110) of the Gini coefficient. These findings offer valuable insights for promoting the structural optimization and spatial distribution of healthcare resources in Shanghai. • Evaluate healthcare resource allocation using quantitative analytics. • Identify spatial clustering patterns in healthcare distribution. • Assess fairness across population, economy, geography, and green spaces. • Detect regional disparities with central urban resource concentration. • Provide insights for optimizing healthcare structure and distribution.

Source: Healthcare Analytics

DOI: 10.1016/j.health.2025.100400

Authors: Hong-Yan Li, Jing Guo, Chuang-Hao Yang

Institution: Shanghai University of Engineering Science

Language: English

Topic: Healthcare Systems and Reforms

Subfield: Finance

Field: Economics, Econometrics and Finance

Domain: Social Sciences

SDGs: Good health and well-being, Reduced inequalities

Open Access status: gold

Estimated APC paid: $1,790